class_docstr: Dataset builder that allows building a dataset without defining a class.
code: def _split_generators(
      self, dl_manager: download.DownloadManager
  ) -> Dict[splits_lib.Split, split_builder_lib.SplitGenerator]:
      del dl_manager
      split_generators = {}
      for split_name, dataset in self._split_datasets.items():
          if isinstance(dataset, tf.data.Dataset):
              split_generators[
                  split_name
              ] = self._generate_examples_tf_data(dataset)
          elif isinstance(dataset, Iterable):
              split_generators[
                  split_name
              ] = self._generate_examples_iterator(dataset)
          elif isinstance(dataset, beam.PTransform) or isinstance(
              dataset, beam.PCollection
          ):
              split_generators[split_name] = dataset
          else:
              raise ValueError(
                  f"Dataset type {type(dataset)} not supported."
              )
      return split_generators
  
imports: from __future__ import annotations
  from collections.abc import Iterable, Mapping
  import typing
  from typing import Any, Dict
  from absl import logging
  from etils import epath
  from tensorflow_datasets.core import dataset_builder
  from tensorflow_datasets.core import dataset_info
  from tensorflow_datasets.core import dataset_utils
  from tensorflow_datasets.core import download
  from tensorflow_datasets.core import features
  from tensorflow_datasets.core import file_adapters
  from tensorflow_datasets.core import split_builder
  from tensorflow_datasets.core import splits
  from tensorflow_datasets.core import utils
  from tensorflow_datasets.core.utils.lazy_imports_utils import apache_beam
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
label_desc: Use image node_id 3 for calling the AdhocBuilder obj's underlying member method code with example usage: obj._split_generators(dl_manager) and returns: split_generators